# Source: https://github.com/HobbitLong/SupContrast

import torch
import torch.nn as nn
import torch.nn.functional as F


class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, in_planes, planes, stride=1, is_last=False):
        super(BasicBlock, self).__init__()
        self.is_last = is_last
        self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(planes)

        self.shortcut = nn.Sequential()
        if stride != 1 or in_planes != self.expansion * planes:
            self.shortcut = nn.Sequential(
                nn.Conv2d(in_planes, self.expansion * planes, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(self.expansion * planes)
            )

    def forward(self, x):
        out = F.relu(self.bn1(self.conv1(x)))
        out = self.bn2(self.conv2(out))
        out += self.shortcut(x)
        preact = out
        out = F.relu(out)
        if self.is_last:
            return out, preact
        else:
            return out


class Bottleneck(nn.Module):
    expansion = 4

    def __init__(self, in_planes, planes, stride=1, is_last=False):
        super(Bottleneck, self).__init__()
        self.is_last = is_last
        self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, bias=False)
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(planes)
        self.conv3 = nn.Conv2d(planes, self.expansion * planes, kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm2d(self.expansion * planes)

        self.shortcut = nn.Sequential()
        if stride != 1 or in_planes != self.expansion * planes:
            self.shortcut = nn.Sequential(
                nn.Conv2d(in_planes, self.expansion * planes, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(self.expansion * planes)
            )

    def forward(self, x):
        out = F.relu(self.bn1(self.conv1(x)))
        out = F.relu(self.bn2(self.conv2(out)))
        out = self.bn3(self.conv3(out))
        out += self.shortcut(x)
        preact = out
        out = F.relu(out)
        if self.is_last:
            return out, preact
        else:
            return out


# class ResNet(nn.Module):
#     def __init__(self, block, num_blocks, in_channel=3, zero_init_residual=False):
#         super(ResNet, self).__init__()
#         self.in_planes = 64

#         self.conv1 = nn.Conv2d(in_channel, 64, kernel_size=3, stride=1, padding=1,
#                                bias=False)
#         self.bn1 = nn.BatchNorm2d(64)
#         self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1)
#         self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2)
#         self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2)
#         self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2)
#         self.avgpool = nn.AdaptiveAvgPool2d((1, 1))

#         for m in self.modules():
#             if isinstance(m, nn.Conv2d):
#                 nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
#             elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
#                 nn.init.constant_(m.weight, 1)
#                 nn.init.constant_(m.bias, 0)

#         # Zero-initialize the last BN in each residual branch,
#         # so that the residual branch starts with zeros, and each residual block behaves
#         # like an identity. This improves the model by 0.2~0.3% according to:
#         # https://arxiv.org/abs/1706.02677
#         if zero_init_residual:
#             for m in self.modules():
#                 if isinstance(m, Bottleneck):
#                     nn.init.constant_(m.bn3.weight, 0)
#                 elif isinstance(m, BasicBlock):
#                     nn.init.constant_(m.bn2.weight, 0)

#     def _make_layer(self, block, planes, num_blocks, stride):
#         strides = [stride] + [1] * (num_blocks - 1)
#         layers = []
#         for i in range(num_blocks):
#             stride = strides[i]
#             layers.append(block(self.in_planes, planes, stride))
#             self.in_planes = planes * block.expansion
#         return nn.Sequential(*layers)

#     def forward(self, x, layer=100):
#         out = F.relu(self.bn1(self.conv1(x)))
#         out = self.layer1(out)
#         out = self.layer2(out)
#         out = self.layer3(out)
#         out = self.layer4(out)
#         out = self.avgpool(out)
#         out = torch.flatten(out, 1)
#         return out


# def resnet18(**kwargs):
#     return ResNet(BasicBlock, [2, 2, 2, 2], **kwargs)


# def resnet34(**kwargs):
#     return ResNet(BasicBlock, [3, 4, 6, 3], **kwargs)


# def resnet50(**kwargs):
#     return ResNet(Bottleneck, [3, 4, 6, 3], **kwargs)


# def resnet101(**kwargs):
#     return ResNet(Bottleneck, [3, 4, 23, 3], **kwargs)


# model_dict = {
#     'resnet18': [resnet18, 512],
#     'resnet34': [resnet34, 512],
#     'resnet50': [resnet50, 2048],
#     'resnet101': [resnet101, 2048],
# }


class LinearBatchNorm(nn.Module):
    """Implements BatchNorm1d by BatchNorm2d, for SyncBN purpose"""
    def __init__(self, dim, affine=True):
        super(LinearBatchNorm, self).__init__()
        self.dim = dim
        self.bn = nn.BatchNorm2d(dim, affine=affine)

    def forward(self, x):
        x = x.view(-1, self.dim, 1, 1)
        x = self.bn(x)
        x = x.view(-1, self.dim)
        return x


class SupConResNet(nn.Module):
    """backbone + projection head"""
    def __init__(self, encoder, dim_in, head='mlp', feat_dim=128):
        super(SupConResNet, self).__init__()
        self.encoder = encoder
      
        if head == 'linear':
            self.head = nn.Linear(dim_in, feat_dim)
        elif head == 'mlp':
            self.head = nn.Sequential(
                nn.Linear(dim_in, dim_in),
                nn.ReLU(inplace=True),
                nn.Linear(dim_in, feat_dim)
            )
        else:
            raise NotImplementedError(
                'head not supported: {}'.format(head))

    def forward(self, x):
        feat = self.encoder(x)
        feat = F.normalize(self.head(feat), dim=1)
        return feat


class SupCEResNet(nn.Module):
    """encoder + classifier"""
    def __init__(self, encoder, num_classes=10):
        super(SupCEResNet, self).__init__()
        self.encoder = encoder
        dim_in = list(encoder.named_modules())[-1][1].in_features
        self.fc = nn.Linear(dim_in, num_classes)

    def forward(self, x):
        return self.fc(self.encoder(x))


# class LinearClassifier(nn.Module):
#     """Linear classifier"""
#     def __init__(self, encoder, num_classes=10):
#         super(LinearClassifier, self).__init__()
#         dim_in = list(encoder.named_modules())[-1][1].in_features
#         self.fc = nn.Linear(dim_in, num_classes)

#     def forward(self, features):
#         return self.fc(features)
class LinearClassifier(nn.Module):
    """Linear classifier"""
    def __init__(self, feat_dim, num_classes=10):
        super(LinearClassifier, self).__init__()
        self.fc = nn.Linear(feat_dim, num_classes)

    def forward(self, features):
        return self.fc(features)
